complete generate.py & readme

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diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/README.md b/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/README.md
@@ -0,0 +1,151 @@
+# Qwen2-VL
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen-VL models on [Intel GPUs](../../../README.md).  For illustration purposes, we utilize the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) (or [Qwen/Qwen2-VL-7B-Instruct](https://www.modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct) for ModelScope) as a reference Qwen2-VL model.
+
+## 0. Requirements
+To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen2-VL model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
+pip install transformers==4.45.0 # install transformers which supports Qwen2-VL
+pip install accelerate==0.33.0
+pip install qwen_vl_utils
+pip install "trl<0.12.0"
+
+# [optional] only needed if you would like to use ModelScope as model hub
+pip install modelscope==1.21.0
+pip install addict simplejson python-dateutil sortedcontainers
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11 libuv
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
+pip install transformers==4.45.0 # install transformers which supports Qwen2-VL
+pip install accelerate==0.33.0
+pip install qwen_vl_utils
+pip install "trl<0.12.0"
+
+# [optional] only needed if you would like to use ModelScope as model hub
+pip install modelscope==1.21.0
+pip install addict simplejson python-dateutil sortedcontainers
+```
+
+### 2. Configures OneAPI environment variables for Linux
+
+> [!NOTE]
+> Skip this step if you are running on Windows.
+
+This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
+
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```bash
+export SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU and Intel Arc™ A-Series Graphics</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+
+> [!NOTE]
+> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```bash
+# for Hugging Face model hub
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH
+
+# for ModelScope model hub
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH --modelscope
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the Qwen2-VL model (e.g. `Qwen/Qwen2-VL-7B-Instruct`) to be downloaded, or the path to the checkpoint folder. It is default to be `'Qwen/Qwen2-VL-7B-Instruct'`.
+- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'Describe this image.'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
+
+#### Sample Output
+##### [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)
+```log
+Inference time: xxxx s
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Prompt --------------------
+Describe this image.
+-------------------- Output --------------------
+The image depicts a young child holding a small white teddy bear. The teddy bear is dressed in a pink outfit, which includes a pink skirt and a
+```
+
+```log
+Inference time: xxxx s
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Prompt --------------------
+请描述这幅图片
+-------------------- Output --------------------
+这是一张小女孩抱着一个白色的小熊玩偶的图片。小女孩穿着一件粉红色的条纹连衣裙，手里抱着的小熊玩偶
+```
+
+The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)):
+
+<a href="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg"><img width=400px src="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg" ></a>
diff --git a/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/generate.py b/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/generate.py
@@ -19,10 +19,8 @@
 import argparse
 import numpy as np
 
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
 from ipex_llm.transformers import Qwen2VLForConditionalGeneration
 from qwen_vl_utils import process_vision_info
-from ipex_llm import optimize_model
 
 
 if __name__ == '__main__':
@@ -42,16 +40,23 @@
                         help="Use models from modelscope")
 
     args = parser.parse_args()
+    if args.modelscope:
+        from modelscope import AutoProcessor
+        model_hub = 'modelscope'
+    else:
+        from transformers import AutoProcessor
+        model_hub = 'huggingface'
+
     model_path = args.repo_id_or_model_path
 
     model = Qwen2VLForConditionalGeneration.from_pretrained(model_path,
-                                                 load_in_4bit=True,
                                                  trust_remote_code=True,
                                                  torch_dtype='auto',
+                                                 modules_to_not_convert=["visual"],
+                                                 load_in_4bit=True,
                                                  low_cpu_mem_usage=True,
-                                                 use_cache=True,)
-
-    model = optimize_model(model, low_bit='sym_int4', modules_to_not_convert=["visual"])
+                                                 use_cache=True,
+                                                 model_hub=model_hub)
 
     # Use .float() for better output, and use .half() for better speed
     model = model.half().to("xpu")

diff --git a/python/llm/src/ipex_llm/transformers/model.py b/python/llm/src/ipex_llm/transformers/model.py
@@ -842,4 +842,4 @@ class AutoModelForTokenClassification(_BaseAutoModelClass):
 
 if transformers.__version__ >= '4.45.0':
     class Qwen2VLForConditionalGeneration(_BaseAutoModelClass):
-        HF_MODEL = transformers.Qwen2VLForConditionalGeneration
+        HF_Model = transformers.Qwen2VLForConditionalGeneration